Identifying sensory inputs affecting working memory load of an individual

ABSTRACT

In an aspect of the invention, a method of identifying sensory inputs affecting working memory load of an individual is provided. The method comprises monitoring (S 101 ) working memory load of the individual using a sensor device, detecting (S 102 ) an increase in the working memory load of the individual, and identifying (S 103 ), in response to the detected increase, at least one sensory input affecting the working memory load of the individual.

CROSS REFERENCE TO RELATED APPLICATION(S)

This application is a 35 U.S.C. § 371 National Stage of InternationalPatent Application No. PCT/EP2016/075748, filed Oct. 26, 2016,designating the United States.

TECHNICAL FIELD

The invention relates to a method of identifying sensory inputsaffecting working memory load of an individual, a device for identifyingsensory inputs affecting working memory load of an individual, acorresponding computer program, and a corresponding computer programproduct.

BACKGROUND

Concentration makes people less susceptible to distraction, but involvesconsiderable cognitive straining. The extent to which people can focuson a certain task depends on the exact nature and difficulty of thattask and the modality (e.g., visual, verbal) of the distraction incomparison with the task.

Working memory is the small amount of information in a person's mindthat they can readily access. The working memory is used for cognitivetasks such as reasoning and problem solving. The information retained inone's working memory is temporary; it disappears unless it is stored inthe long-term memory. Also, the amount of information that can be storedin the working memory is limited. Competing sensory inputs (i.e.,distractions) can result in high working memory load, which can limitone's cognitive capabilities. Measurement of pupil diameter can be usedto obtain information about demand for working memory in differentcognitive activities.

Cognitive load can be exemplified by measuring pupillary response as aperson is undergoing various mental processes. Studies show the mentaleffort required to perform a calculation, and how this effort changesthroughout the process of the task.

For instance, a study has been performed that attempted to correlatedifferent pupil measurements, namely fixation duration and pupil size,with memory load and processing load. The study discovered that theincrease in fixation duration with number of targets both within andabove working memory capacity suggests that in free viewing, fixationduration is sensitive to actual memory load as well as to processingload, whereas pupil size is indicative of processing load only. Hence,it was concluded that fixation duration is selective to memory load fortargets. In contrast, changes in pupil size are too slow for isolatinginstances of memory accumulation such as target encoding in the freeviewing search task. Pupil size most likely reflects an overallprocessing load which incorporates several cognitive processes.

The effect that the performing of tasks has on the human pupil is called‘task-evoked pupillary response’. Recent work, for instance “Measuringthe Task-Evoked Pupillary Response with a Remote Eye Tracker” by J.Klingner, R. Kumar, and P. Hanrahan, in Proceedings of the 2008symposium on Eye tracking research & applications (ETRA '08), pages69-72, ACM New York, 2008, has found that remote video eye trackers haveenough precision to be used for detailed task-evoked pupillarymeasurements.

An electroencephalogram (EEG) is a technique whereby electrical signalson the scalp, forehead or in-ear regions are measured for determiningbrain activity. In the literature it has been shown that EEGmeasurements can be linked to cognitive tasks. For instance, themeasuring of EEG signals can be used to classify a mental task withrelatively high accuracy. As an example, mentally letter writing, inwhich subjects were instructed to mentally compose a letter to a friendwithout vocalizing, could be distinguished from the task of mentallymultiplying two multi-digit numbers, such as 49 times 78.

A number of devices and associated methods are available to gatherknowledge of what sensory inputs are in the local area of a subject oruser, i.e., what is nearby that could be sensed by the user. Someexamples are given in the following.

Imaging sensors can be used to identify objects within the field of viewof the sensors. The use of image sensors in object recognition hasbecome particularly powerful due to the development of machine-learningalgorithms which can identify objects in images or video with a highdegree of accuracy.

Lightfield cameras are able to capture data about the lightfieldemanating from a scene. As such more data can be obtained than if just asingle plane is recorded, as per a standard camera. As such 3D imagescan be obtained using a single camera with a single lens.

Sensors based on LIDAR (“Light Detection And Ranging”) are able to scantheir environment to gain a 3D map of their environment, and thusprovide data to an algorithm that can identify objects. Recently ‘onchip’ implementations or LIDAR has been prototyped, providing a path tolow cost implementations as low as $10. RADAR (“Radio Detection AndRanging”) also has the capability to identify objects.

Audio sensors, i.e., microphones, can also be used to identify differentevents. Algorithms have been developed for identifying events based onthe sounds they produce. When combined with directional microphones thisallows a user to associate an action (event) and location with an event.

Devices that are able to identify smell (so called electronic noses)have been developed. While these vary greatly in terms of the underlyingtechnology some extremely powerful devices are now available at massmarket prices, and in fact have been incorporated into devices such asthose to detect use of alcohol or marijuana.

A number of devices and associated methods are available to obscuresensory inputs from a user—i.e., either mask or remove a sensory inputsuch that it is no longer noticed by the user. Some examples are givenin the following.

Augmented Reality (AR) is a technology which allows to change how thephysical environment appears to the user, by artificially addingcomputer generated content that blends with the environment. This can bevisual content, e.g., using AR headsets. AR has potential inentertainment (media, gaming) but also in education and for professionaluse.

Recently a number of ‘active hearing’ devices have been released to themarket or announced. Examples include, the ‘Here One’ from Doppler Labsand the ‘Pilot’ from Waverly Labs. Whilst the Pilot demonstrates theability to add intelligence to hearing devices (purportedly it willtranslate between users speaking different languages), the Here Onemodifies the level to which various sounds are either heard or notheard.

Devices that are able to synthesise odour are available in the market orhave been announced, an example being the ‘Cyrano’. Other groups haveworked on screens that can project smells by using fans to locate asmell on a certain part of a screen. Odour can be masked by anotherodour where a first smell is replaced by a stronger second smell, or itcan masked by using white odour. Scientists discovered that similarly towhite light, where the combination of many wavelengths produce white(seemingly colourless) light, mixtures with many odour components canproduce a smell that is difficult to distinguish.

Content filtering is a way of altering or restricting digital content,such as advertisement, inappropriate material, or potentially hostileapplications. Content filtering software is used for different reasons,one of which can be the blocking of unwanted distractions while browsingthe Internet. The use of AR offers the possibility of “real world” adMocking, which is the Mocking of e.g., brand logos and advertisements inthe physical environment of the AR device user.

Productivity apps are computer programs that help increased productivityand improve concentration and focus, e.g., by filtering or Mocking ofdistracting content, by setting timers, or by scheduling work and breakstaking into account the user's attention span.

However, existing solutions have problems.

As regards identification and removal of potentially distracting sensoryinputs:

-   -   working memory load: existing solutions require the user to        think actively about what is distracting them, being itself        detrimental to the working memory load of the user;    -   universality: existing solutions only target certain types of        distracters (e.g., block website advertisements) or focus on        just one of the user's sensory inputs (e.g., only block audio        inputs).

As regards feedback/control:

-   -   focus detection: existing solutions do not directly measure the        actual working memory load of the user, and thus have less        information about the need for the removal of distracters and        what distracters should be removed;    -   effect of removal: existing solutions do not directly measure        the effectiveness of the removal of individual distracters on        the user's working memory load.

SUMMARY

An object of the present invention is to solve or, at least mitigate,the problem in the art of how to provide method of identifying sensoryinputs affecting working memory load of an individual.

This object is attained in a first aspect of the invention by a methodof identifying sensory inputs affecting working memory load of anindividual. The method comprises monitoring working memory load of theindividual using a sensor device, detecting an increase in the workingmemory load of the individual and identifying, in response to thedetected increase, at least one sensory input affecting the workingmemory load of the individual

This object is attained in a second aspect of the invention by a devicefor identifying sensory inputs affecting working memory load of anindividual. The device comprises a sensor device configured to monitorworking memory load of the individual, and a processing unit configuredto detect an increase in the working memory load of the individual andto identify, in response to detecting the increase, at least one sensoryinput affecting the working memory load of the individual.

Hence, a sensor device is used to monitor working memory load of anindividual. In an example, the individual is seated in front of hercomputer and a built-in camera of the computer is used as a sensordevice for monitoring the working memory load by monitoring one or botheyes of the individual.

The example camera may further be used in combination with a processingunit of the computer for detecting any increase in the working memoryload of the individual. For instance, the processing unit may, duringthe monitoring by the camera of the individual, detect a change inworking memory load in the form of, e.g., a sudden increase in pupilsize of one or both eyes of the individual. Such detection indicates anincrease in working memory load of the individual.

Upon such detection, the processing unit of the computer may identify atleast one sensory input affecting the working memory load of theindividual. For example, a built-in microphone of the computer is usedin combination with the processing unit and registers a sound beinglikely to be the sensory input causing the detected increase in workingmemory load.

This identification of sensory inputs affecting the working memory loadof the individual is highly advantageous and can, as will be discussedin various embodiments in the following, be used for many purposes.

In an embodiment of the invention, after having identified at least onesensory input affecting the working memory load of the individual, theprocessing unit is configured to diminish an impact that the identifiedat least one sensory input has on the working memory load of theindividual.

For instance, if the individual resides in a connected home where manyfunctions such as heating, light and blind control, activation ofkitchen equipment etc., are connected to a local area network, such as aWireless Local Area Network (WLAN), along with any computers, tablets,smart phones, etc, actions may be initiated to mitigate the negativeeffect which the sensory inputs have on the working memory load of theindividual.

Now, if a change in working memory load is monitored and detected by acamera and processing unit of a tablet or smartphone currently operatedby the individual, by observing an increased pupil diameter, theprocessing unit of the individual's tablet may identify—by measuringambient temperature or communicating with a heat control system—that itis the heating of the home that is set to a too high temperature whichnegatively affects the working memory load of the individual.

As a result thereof, the processing unit of the tablet transmits acontrol signal via the WLAN to a home heating control system, i.e., asource of the identified sensory input, to lower the temperature tothereby advantageously diminish an impact that the identified at leastone sensory input has on the working memory load of the individual.

It is noted that in this example, a slightly too high indoor temperaturewould typically not cause a sudden change in the working memory load buta change that must be monitored over an extended period of time.

In another example, the individual wears a pair of headphones equippedwith noise-reducing capability, and the camera and processing unit ofthe tablet that the individual currently is operating detect an increasein pupil size, thereby indicating an increased working memory load ofthe individual.

The noise-reducing headphones are further equipped with a microphoneacting as a sensory input detection device in combination with aprocessing unit of the headphones for registering disturbing backgroundnoise. After receiving a wireless or wired signal from the computerindicating that an increase in working memory load has been detected,the processing unit of the noise-reducing headphones initiates acountermeasure to the identified sensory input by subjecting theindividual to a signal which is an out-of-phase representation of thebackground noise, thereby effectively cancelling out the backgroundnoise.

In another embodiment of the invention, the identifying of a sensoryinput affecting the working memory load of the individual isadvantageously performed by identifying a sensory input coinciding intime with the detected increase in the working memory load, wherein theidentified at least one sensory input is considered to be the sensoryinput affecting the working memory load of the individual.

This is advantageous if a sensory input occurs which gives rise to arather sudden increase in working memory load such as, e.g., when theindividual is subjected to a sound or a light having an immediate impacton the working memory load. However, in case the individual is subjectedto a less evident sensory input, such as, e.g., a change in temperaturewhich generally is a much slower process, a processing unit acting as asensory input detection device may have to evaluate events which havehappened during a recent period of time, for instance during the last 10minutes. As an example, the sensory input detection device may be atemperature sensor used in combination with the processing unit forevaluating whether an increase in temperature has occurred over the last10 minutes, or even a gas sensor evaluating whether a particularchemical substance is present in ambient air.

In another embodiment of the invention, for each identified sensoryinput, a measure with which the identified at least one sensory inputaffects the working memory load of the individual is advantageouslydetermined. For instance, the measure may be configured to assume avalue between 10 and 100, where 10 would imply a small impact, while 100would imply a major impact on the working memory load of the individual.

In another embodiment of the invention, the measure associated with aparticular sensory input may be stored in a database for later use. Inpractice, in a situation where the individual is subjected to aplurality of sensory inputs, it may be difficult to assess whichparticular input(s) affect(s) the individual the most.

In an embodiment of the invention, by utilizing a database comprising ameasure associated with each type of sensory input, the processing unitbeing configured to diminish the impact of the sensory input(s) mayconclude by assessing the database that one or a couple of differentsensory inputs usually affect the individual to higher degree thanothers, and accordingly diminishes the effect of these high-impactsensory inputs.

Advantageously, in an embodiment of the invention, it is possible toutilize a learning phase during which the individual purposely issubjected to different sensory inputs while changes in the workingmemory load of the individual are monitored. Further, a measureassociated with each sensory input is estimated and stored in a databasefor subsequent use.

In the above examples, the change in the individual's work load isdetected by means of a processing unit analysing images captured by acamera. However, other sensor devices are envisaged, such as EEGsensors, electrocardiogram (EKG) sensors, heart rate meters, etc.

Further, as has been discussed with reference to what is known in theart in the above, a number of different sensory input detection devicesmay be envisaged.

Further provided is a computer program comprising computer-executableinstructions for causing a device to perform steps of the methodaccording to the first aspect of the invention, when thecomputer-executable instructions are executed on a processing unitincluded in the device.

Further provided is a computer program product comprising a computerreadable medium, the computer readable medium having the computerprogram of the device embodied thereon.

Generally, all terms used in the claims are to be interpreted accordingto their ordinary meaning in the technical field, unless explicitlydefined otherwise herein. All references to “a/an/the element,apparatus, component, means, step, etc.” are to be interpreted openly asreferring to at least one instance of the element, apparatus, component,means, step, etc., unless explicitly stated otherwise. The steps of anymethod disclosed herein do not have to be performed in the exact orderdisclosed, unless explicitly stated.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is now described, by way of example, with reference to theaccompanying drawings, in which:

FIG. 1 illustrates a device for identifying sensory inputs affectingworking memory load of an individual, according to an embodiment of theinvention;

FIG. 2 shows a top view of the device of FIG. 1 with a user seated infront of the device;

FIG. 3 illustrates a flowchart of a method of identifying sensory inputsaffecting working memory load of an individual, according to anembodiment of the invention;

FIG. 4 illustrates a flowchart of a method of identifying sensory inputsaffecting working memory load of an individual, according to anotherembodiment of the invention;

FIG. 5 shows a top view of the device of FIG. 1 with a user seated infront of the device equipped with a pair of headphones withnoise-reducing capability;

FIG. 6 illustrates a flowchart of a method of identifying sensory inputsaffecting working memory load of an individual, according to yet anotherembodiment of the invention;

FIG. 7 illustrates a flowchart of a method of identifying sensory inputsaffecting working memory load of an individual, according to a furtherembodiment of the invention;

FIG. 8 illustrates a flowchart of a method of identifying sensory inputsaffecting working memory load of an individual, according to yet afurther embodiment of the invention; and

FIG. 9 illustrates a device for identifying sensory inputs affectingworking memory load of an individual, according to another embodiment ofthe invention.

DETAILED DESCRIPTION

The invention will now be described more fully hereinafter withreference to the accompanying drawings, in which certain embodiments ofthe invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided byway of example so that this disclosure will be thorough and complete,and will fully convey the scope of the invention to those skilled in theart. Like numbers refer to like elements throughout the description.

FIG. 1 illustrates a device 10 for identifying sensory inputs affectingworking memory load of an individual according to an embodiment of theinvention. The device is and shows a device exemplified in the form of adesktop computer 10 in a front view, having a screen 11, a camera 12, amicrophone 13, and a loudspeaker 14.

FIG. 2 shows a top view of the desktop computer 10, with a user 20seated in front of it. As can be seen, the camera 12, the microphone 13,and the loudspeaker 14 are operatively coupled to a processing unit 15embodied in the form of one or more microprocessors arranged to executea computer program 16 downloaded to a suitable storage medium 17associated with the microprocessor 15, such as a Random Access Memory(RAM), a Flash memory, a hard disk drive, a cloud service or otherinformation storage devices. The processing unit 15 is arranged tocontrol operation of the desktop computer to when the appropriatecomputer program 16 comprising computer-executable instructions isdownloaded to the storage medium 17 and executed by the processing unit15. The storage medium 17 may also be a computer program productcomprising the computer program 16. Alternatively, the computer program16 may be transferred to the storage medium by means of a suitablecomputer program product, such as a Digital Versatile Disc (DVD) or amemory stick. As a further alternative, the computer program may bedownloaded to the storage medium 17 over a network. The processing unit15 may alternatively be embodied in the form of a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), etc.

As discussed hereinbefore, the computer 10 is in an embodimentconfigured to identify events that have an unwanted effect on the focusof the user, e.g., whilst the user performs a cognitively demandingtask.

As can be seen in FIG. 2, the user 20 of the computer to is, when seatedin front of the screen 11, positioned in a field of view of the camera12. A method of identifying sensory inputs affecting working memory loadof an individual—i.e., the user 20—will be described in the followingwith reference to FIG. 2 and further to FIG. 3 illustrating a flowchartof the method.

A sensor device is used to monitor working memory load of an individualin step S101. In this particular exemplifying embodiment, the sensordevice is embodied by the camera 12 which monitors one or both eyes ofthe user 20. Now, if the user 20 becomes distracted by a sensory input,the working memory load will increase, which typically results in anincrease of the pupils of the user's eyes.

In an embodiment of the invention, it is envisaged that if the diameterof the pupil of one of the user's eyes increase above a threshold value,for instance 0.5 mm, the working memory load of the user 20 isconsidered to have increased. It is further envisaged that differentthreshold values are used; for example, a 0.2 mm increase represents afirst working memory load value A, a 0.4 mm increase represents a secondworking memory load value B, a 0.6 mm increase represents a thirdworking memory load value C, and so on.

In step S102, the camera 12 (or the processing unit 15 analysing imagescaptured by the camera 12), detects an increase in the working memoryload of the user 20, for instance by concluding that the diameter of thepupils of the user's eyes has increased above a certain threshold value.

In response to the detected increase in the user's working memory load,a sensory input detection device—in this particular embodiment beingexemplified by the processing unit 15 receiving signals from themicrophone 13—advantageously identifies one or more sensory inputsaffecting the working memory load of the user 20 in step S103. In thisexample, the built-in microphone 13 of the computer 20 registers a soundbeing likely to be the sensory input causing the detected change inworking memory load of the user 20.

In an embodiment, the sensory input, in this example being a sound, isadvantageously identified by the processing unit 15 to coincide in timewith the detected increase in the working memory load, thereby beingconsidered to be the sensory input causing the increase. Thus, if thesound was recorded just before the increase in working memory load wasdetected, the sound is considered be the sensory input causing theincrease.

In an embodiment, upon having identified a sensory input affecting theworking memory load of the user 20, actions are taken to diminish theimpact that the sensory input has on the working memory load, as will bediscussed in the following.

In the embodiment illustrated in the flowchart of FIG. 4, after themicrophone 13 has recorded a sound being considered to be the sensoryinput causing the increase in working memory load in step S103, theprocessing unit 15 of the computer 10 concludes that the user 20 herselfjust started an audio player of the computer to and advantageouslylowers a sound level being output by the audio player via theloudspeaker 14 of the computer to in step S104 a, since the selectedaudio level is too high and thus affects the working memory load of theuser 20. Hence, in this particular embodiment, the impact that theidentified sensory input has on the working memory load of the user isdiminished using the processing unit 15 controlling the very source ofthe identified sensory input, in this case being the computerloudspeaker 14.

In another embodiment illustrated with reference to FIGS. 5 and 6, theimpact of the sensory input on the user's working memory load isdiminished by initiating a countermeasure to the identified one sensoryinput.

As can be seen in FIG. 5, the user 20 wears a pair of headphones 21which in this example is equipped with noise-reducing capability. Now,the camera 12 of the computer to in cooperation with the processing unit15 monitor and detect an increase in the working memory load of the user20 in steps S101 and S102 as illustrated in FIG. 6, and a processingunit and microphone (not shown) of the headphones 21 serve as a sensoryinput detection device for registering disturbing background noise instep S103, as illustrated with the identified sound 22.

After receiving a wireless or wired signal 23 from the computer toindicating that an increase in working memory load has been detected instep S102, the processing unit of the noise-reducing headphones 21advantageously initiates a countermeasure to the identified sensoryinput by subjecting the individual to a signal 24 which is anout-of-phase representation of the background noise 23 in step S104 b,thereby effectively cancelling out the background noise.

In the above discussed embodiments, the camera 12 is used as a sensordevice for monitoring and detecting an increase in working memory loadof the user 20 in cooperation with the processing unit 15, by detectingchanges in the pupil size of the user's eyes. It may further beenvisaged that the camera 12 is used as a gaze detector with capabilityto track a direction of the user's visual attention and its duration.

Generally, people have a tendency to look at an object when using it fora task—someone working at a laptop will spend most of the task timelooking at the laptop. Hence, if their gaze is suddenly shifted, thismay be an indication that they have been distracted. When gaze isshifted to another object that is providing a sensory input (e.g.,making a sound, displaying changing images, etc.), this may indicatethat that object is causing distraction.

However, people may also shift gaze to concentrate, or for inspiration,such as looking skywards.

By tracking the user's gaze, and in particular recording the times shechanges gaze from a device she is using for her task and toward anobject providing a sensory input, a measurement can be made as to howoften the user is likely being distracted. A signature ‘inspiration’ or‘concentration’ gaze can be learnt for a given user and discounted fromthis measurement.

Further, it is envisaged that a worn device equipped with a camera isused as a sensor device for monitoring and detecting an increase inworking memory load of the user in cooperation with a processing unit ofthe worn device, such as a virtual reality (VR) headset or a GoogleGlass type eyewear.

Moreover, activity tracking sensor devices that is able to identifyspecific activities the user is engaged in and possibly the user'sefficiency in these activities is envisaged.

Similar to gaze, a person's motion can be used to assess the likelihoodthey are being distracted. Hence if the sensor device is a motiontracking sensor, and if the user shifts position often, particular inresponse to an object providing sensory input (e.g., a television) thenthe sensor's output can provide an indication that object isdistracting. Therefore, by monitoring the person's motion, and ifpossible correlating this with objects in the room, then a measure canbe made as to whether they are being distracted and by what.

An activity tracking sensor device may also track the progress of atask, for example number of words written in a document, increase insize of a file of a drawing file, number of cells adjusted in an Excelfile etc.

Various sensor devices other than cameras can be envisaged, e.g., EEGsensors, EKG sensors, heart rate meters, etc.

Different properties may further be combined to detect an increase inthe working memory load of the user, for instance by considering acombination of two or more of pupil size, gaze, heart rate, activity,EEG, etc.

Further, a number of types of devices for identifying sensory inputsaffecting working memory load of an individual can be envisaged, such aslaptops, tablets, smart phones, smart watches (using for instance heartrate as a measure of working memory load), television sets, etc.

In yet another embodiment, the user 20 is during a learning phase of thedevice 10 purposely subjected to different sensory inputs while changesin the working memory load of the individual are monitored. Further, ameasure associated with each particular sensory input is estimated andstored in a database for subsequent use.

For instance, for any given user, a nominal working memory load may berecorded when the user practically is not subjected to any (disturbing)sensory input in her work environment. This lowest working memory loadis denoted “Load_(NOM)”, and corresponds to pupil size denoted“PupilSize_(NOM)”.

Now, the user is subjected to different sensory inputs, and an increasein pupil size of the user's eyes is detected and a correspondingincrease in working memory load, as will be illustrated in Table 1herein below.

Hence, in an embodiment of the invention, a representation of anyidentified sensory input, and the corresponding measure with which saidany identified sensory input affects the working memory load of theindividual, is entered in a database as illustrated in Table 1.

Reference is further made to the flowchart of FIG. 7.

In a first round, the user 20 is subjected to three different soundlevels of music selected from one of her playlists (i.e., music that theuser indeed appreciates) and played through the loudspeaker 14 of hercomputer, while the camera 12 monitors the pupil size of the user's eyesin step S101 and the processing unit 15 detects a 0.1 mm increase, a 0.3mm increase and a 0.5 mm increase, respectively, for the three(increasing) sound levels Sound level 1, Sound level 2, and Sound level3, in step S102, which are considered in step S105 to correspond to a10%, 30% and 50% increase in working memory load with respect toLoad_(NOM). The different sounds are identified by the microphone 13 andthe processing unit 15 in step S103.

Hence, for each identified sensory input of the plurality of sensoryinputs affecting the working memory load of the user 20, a measure withwhich each identified sensory input affects the working memory load ofthe user 20 is determined and entered in the database. This assessmentis typically performed by the processing unit 15, but couldalternatively be performed by the camera 12 itself. The three recordedsounds and their respective impact on the working memory load correspondto Items 1, 2, and 3, in Table 1.

In a second round, the user 20 is subjected to lighting conditionscorresponding to indoor lighting of the office during a winterday.Again, the camera 12 monitors the pupil size of the user's eyes and theprocessing unit 15 detects a 0.2 mm increase, which is considered tocorrespond to a 20% increase in working memory load with respect toLoad_(NOM). This corresponds to Item 4 in Table 1.

In a third round, the user 20 is subjected to the sound of the officeair conditioning system starting. The camera 12 monitors the pupil sizeof the user's eyes and the processing unit 15 detects a 0.1 mm increase,which is considered to correspond to a 10% increase in working memoryload with respect to Load_(NOM). This corresponds to Item 5 in Table 1.

TABLE 1 Recorded sensory inputs vs. increase in working memory load.Increase in working Change in pupil size memory load from Sensory inputfrom nominal size nominal load Sound level 1 of audio PupilSize_(NOM) +0.1 mm Load_(NOM) + 10% player Sound level 2 of audio PupilSize_(NOM) +0.3 mm Load_(NOM) + 30% player Sound level 3 of audio PupilSize_(NOM) +0.5 mm Load_(NOM) + 50% player Wintertime indoor PupilSize_(NOM) + 0.2mm Load_(Nom) + 20% lighting Air condition sound PupilSize_(NOM) + 0.1mm Load_(NOM) + 10%

Table 1 exemplifies five different items, while in a real-life scenario,tens of different sensory inputs may be recorded in order to cover anabundance of situations having potential to occur and thus increase theworking memory load of the user.

It should be noted that a database such as that of Table 1 may be builtwith purposely subjecting the user 20 to sensory inputs, but can bebuilt while the user 20 is “naturally” subjected to the sensory inputs.Further, the naturally occurring sensory inputs can be added to adatabase comprising sensory inputs to which the user 20 purposely hasbeen subjected.

In a further embodiment illustrated with reference to the flowchart ofFIG. 8, the database of Table 1 is utilized to select which out of anumber of sensory inputs that the user 20 is subjected to should bediminished to effectively decrease the working memory load of the user20.

If an increase in working memory load of the user 20 is detected, somesensory inputs (i.e., sounds, visual inputs, and potentially evenodours) may be removed or obscured in order to reduce the load on theuser's working memory.

In other words—as distractions taking place within the locality of theuser (i.e., within a distance that those activities can impact thesenses of the user) provide sensory inputs that do not contribute to theexecution of the user's task, the presence of these sensory inputs maybe mitigated or even eliminated.

Assuming that that the user 20 of FIG. 2 is subjected to a plurality ofsensory inputs, for instance those listed in Table 1, as identified bythe microphone 13 as regards the audible sensory inputs and a photometer(not shown) identifying the indoor lighting in step S103 after anincrease in load has been detected by the processing unit 15 in stepS102. In such scenario, a problem which may arise is related todistinguishing which of the sensory inputs affects the user 20 the most.

By turning to the database of Table 1 in step S103 a—assuming that theaudio player of the computer 10 outputs music at Sound level 2—theprocessing unit 15 concludes that the audio player playing at Soundlevel 2 affects the user 20 as much as the indoor lighting and the soundof the air condition do jointly.

The processing unit 15 of the computer 10 may hence determine that theaudio player is to be turned off in step S104 a, or at least that itsoutput sound level should be reduced, thereby advantageously decreasingthe working memory load of the user 20.

FIG. 9 illustrates a device 10 for identifying identify sensory inputsaffecting working memory load of an individual. The device 10 comprisesmonitoring means 30 adapted to monitor working memory load of theindividual, detecting means 31 adapted to detect an increase in theworking memory load of the individual, and identifying means 32 adaptedto identify, in response to detecting the increase, at least one sensoryinput affecting the working memory load of the individual.

The monitoring means 30, detecting means 31 and identifying means 32 maycomprise communications interface(s) for receiving and providinginformation, and further a local storage for storing data, and may (inanalogy with that previously discussed) be implemented by a processorembodied in the form of one or more microprocessors arranged to executea computer program downloaded to a suitable storage medium associatedwith the microprocessor, such as a RAM, a Flash memory or a hard diskdrive.

The invention has mainly been described above with reference to a fewembodiments. However, as is readily appreciated by a person skilled inthe art, other embodiments than the ones disclosed above are equallypossible within the scope of the invention, as defined by the appendedpatent claims.

The invention claimed is:
 1. A method of identifying sensory inputsaffecting working memory load of an individual, comprising: monitoringworking memory load of the individual using a sensor device; detectingan increase in the working memory load of the individual; andidentifying, in response to detecting the increase, at least one sensoryinput affecting the working memory load of the individual.
 2. The methodof claim 1, wherein the identifying at least one sensory input affectingthe working memory load of the individual comprises: identifying atleast one sensory input coinciding in time with the detected increase inthe working memory load.
 3. The method of claim 1, further comprising:diminishing an impact that the identified at least one sensory input hason the working memory load of the individual.
 4. The method of claim 3,wherein the diminishing of the impact comprises: controlling a source ofthe identified at least one sensory input to diminish an impact that theidentified at least one sensory input has on the working memory load ofthe individual.
 5. The method of claim 3, wherein the diminishing of theimpact comprises: initiating a countermeasure to the identified at leastone sensory input to diminish an impact that the identified at least onesensory input has on the working memory load of the individual.
 6. Themethod of claim 1, wherein the identifying of at least one sensory inputaffecting the working memory load of the individual comprises:identifying a plurality of sensory inputs affecting the working memoryload of the individual; and wherein the method further comprises:determining, for each identified sensory input of the plurality ofsensory inputs affecting the working memory load of the individual, ameasure with which said each identified sensory input affects theworking memory load of the individual.
 7. The method of claim 6, whereinthe identifying of at least one sensory input affecting the workingmemory load of the individual comprises: subjecting the individual tothe plurality of sensory inputs.
 8. The method of claim 6, wherein thedetermining of a measure with which said each identified sensory inputaffects the working memory load of the individual further comprises:entering a representation of any identified sensory input, and thecorresponding measure with which said any identified sensory inputaffects the working memory load of the individual, in a database.
 9. Themethod of claim 6, wherein the diminishing of an impact that theidentified at least one sensory input has on the working memory load ofthe individual comprises: assessing the database to determine, if aplurality of sensory inputs are identified, which of the identifiedsensory inputs have a high impact on the working memory load; whereinthe impact of one or more of the determined high-impact sensory inputson the working memory load of the individual is diminished.
 10. Themethod of claim 1, wherein the identified sensory input comprises one ormore of: audible input, visual input, changes in temperature, changes inhumidity, odours.
 11. The method of claim 1, wherein the sensor deviceconfigured to monitoring working memory load of the individual isselected from a group comprising: an imaging sensor, a heart rate meter,an electroencephalogram (EEG) sensor, an electrocardiogram (EKG) sensor.12. A computer program product comprising a non-transitory computerreadable medium storing a computer program comprisingcomputer-executable instructions for causing a device to perform thesteps recited in claim 1 when the computer-executable instructions areexecuted on a processing unit included in the device.
 13. A device foridentifying sensory inputs affecting working memory load of anindividual, the device comprising: a sensor device configured to monitorworking memory load of the individual; and a processing unit configuredto detect an increase in the working memory load of the individual andto identify, in response to detecting the increase, at least one sensoryinput affecting the working memory load of the individual.
 14. Thedevice of claim 13, wherein the processing unit is configured to, whenidentifying at least one sensory input affecting the working memory loadof the individual: identify at least one sensory input coinciding intime with the detected increase in the working memory load.
 15. Thedevice of claim 13, the processing unit being configured to: diminish animpact that the identified at least one sensory input has on the workingmemory load of the individual.
 16. The device of claim 15, theprocessing unit being configured to: control a source of the identifiedat least one sensory input to diminish an impact that the identified atleast one sensory input has on the working memory load of theindividual.
 17. The device of claim 15, the processing unit beingconfigured to: initiate a countermeasure to the identified at least onesensory input to diminish an impact that the identified at least onesensory input has on the working memory load of the individual.
 18. Thedevice of claim 13, wherein the processing unit is configured to, whenidentifying at least one sensory input affecting the working memory loadof the individual: identify a plurality of sensory inputs affecting theworking memory load of the individual; and wherein the processing unitfurther is configured to: determine, for each identified sensory inputof the plurality of sensory inputs affecting the working memory load ofthe individual, a measure with which said each identified sensory inputaffects the working memory load of the individual.
 19. The device ofclaim 18, further being configured to: subject the individual to theplurality of sensory inputs.
 20. The device of claim 18, the processingunit further being configured to, when determining a measure with whichsaid each identified sensory input affects the working memory load ofthe individual: enter a representation of any identified sensory input,and the corresponding measure with which said any identified sensoryinput affects the working memory load of the individual, in a database.21. The device of claim 18, wherein the processing unit further isconfigured to, when diminishing an impact that the identified at leastone sensory input has on the working memory load of the individual:assess the database to determine, if a plurality of sensory inputs areidentified, which of the identified sensory inputs have a high impact onthe working memory load; wherein the impact of one or more of thedetermined high-impact sensory inputs on the working memory load of theindividual is diminished.
 22. The device of claim 13, wherein theidentified sensory input comprises one or more of: audible input, visualinput, changes in temperature, changes in humidity, odours.
 23. Thedevice of claim 13, wherein the sensor device configured to monitoringworking memory load of the individual is selected from a groupcomprising: an imaging sensor, a heart rate meter, anelectroencephalogram (EEG) sensor, an electrocardiogram (EKG) sensor.